User-tailored roadway complexity awareness

ABSTRACT

A roadway complexity awareness system for a vehicle, comprising a processor, an augmented reality interface disposed in communication with the processor, and a memory for storing executable instructions. The processor is programmed to execute the instructions to receive roadway status information from an infrastructure processor associated with a roadway, obtain, from a vehicle sensory system, sensory information indicative of roadway route complexity, and determine a likelihood of roadway route complexity based on the roadway status information and the sensory information. The system may select an augmented reality message indicative of the roadway route complexity, and generate the augmented reality message using an augmented reality interface device, wherein the augmented reality message is visible to a vehicle operator.

TECHNICAL FIELD

The present disclosure relates to infrastructure to vehiclecommunication systems, and more particularly, to an infrastructure tovehicle communication system user-tailored for roadway complexityawareness.

BACKGROUND

Appropriate awareness of roadway complexity including regulated shiftsin lanes due to construction zones or barriers, is particularlyimportant for convenience and user experience.

Complex situations encountered on the roadway may cause a driver toabruptly reduce vehicle speed if not provided appropriate guidance.Roadway complexity can include road construction, detours, temporarylane diversions, and other similar situations. Conventional systems foralerting drivers to dynamically changing roadway complexity includeinfrastructure-to-vehicle (I2V) communication sent to the vehicle frominfrastructure computing systems associated with traffic barriers orlane shift signs to communicate location of upcoming roadway complexity.However, the user may not be made aware of the complex road conditionsin an appropriate way that does not divert attention from vehicleoperation. Moreover, conventional systems may not provide informationtailored to the conditional state.

In addition, there are situations where drivers, including novice andadvanced age drivers, may operate their vehicle in unfamiliar areas atnight, or under difficult weather conditions, and may be less likely tojudge approaching complex roadway areas requiring vehicle slow down orlane shifts. It may be advantageous to provide enhanced means forautomated roadway complexity awareness that tailor messages and roadwayinformation according to the vehicle, driver, and operationalenvironment.

It is with respect to these and other considerations that the disclosuremade herein is presented.

BRIEF DESCRIPTION OF THE DRAWINGS

The detailed description is set forth with reference to the accompanyingdrawings. The use of the same reference numerals may indicate similar oridentical items. Various embodiments may utilize elements and/orcomponents other than those illustrated in the drawings, and someelements and/or components may not be present in various embodiments.Elements and/or components in the figures are not necessarily drawn toscale. Throughout this disclosure, depending on the context, singularand plural terminology may be used interchangeably.

FIG. 1 depicts a functional schematic of a roadway complexity awarenesssystem in accordance with the present disclosure.

FIG. 2 illustrates an example computing environment in which techniquesand structures for providing the systems and methods disclosed hereinmay be implemented.

FIG. 3 depicts an example view of a roadway as observed by a user of thevehicle in accordance with the present disclosure.

FIG. 4 depicts an example message delivered to a vehicle operator usingthe system of FIG. 1 in accordance with the present disclosure.

FIG. 5 depicts a flow diagram of an example method for generating anaugmented reality message using the system of FIG. 1 in accordance withthe present disclosure.

DETAILED DESCRIPTION

The disclosure will be described more fully hereinafter with referenceto the accompanying drawings, in which example embodiments of thedisclosure are shown, and not intended to be limiting.

FIG. 1 depicts a flow diagram illustrating a systematic process forgenerating and delivering roadway complexity messages to a vehicle userusing a roadway complexity awareness system 100, in accordance with thepresent disclosure. The roadway complexity awareness system 100 caninclude a domain assessment block 120 that receives dynamic environmentdata 105, determines the presence of oncoming roadway complexity, andoutputs the information to a domain assessment block 130 to generateuser enhanced guidance (UEG) personalization at block 140 that generatesoutput messages that are customized according to vehicle, driver,environmental factors. The messages may inform a user 150 of theapproaching roadway complexity.

More particularly, the roadway complexity awareness system 100 may beconfigured and/or programmed to provide tailored user awareness forvehicle drivers. The domain assessment block 120 may include a RouteComplexity en route (RCR) determination module 125 configured and/orprogrammed to determine situations where there may exist a likelihood ofroadway complexity associated with barriers and lane shifts, and otherevents encountered on the roadway while driving.

The RCR determination module 125 may receive dynamic environment data105 that can include vehicle, driver, and environment data 110,infrastructure-to-vehicle (I2V) data 112, and telematics connectivitydata 115 obtained from vehicle and connected systems. In one embodiment,the roadway complexity awareness system 100 fuses the dynamicenvironment data 105, and assesses a likelihood of road complexity usinga computational intelligence decision model configured and/or programmedto activate an input for graduated Augmented Reality awareness. Thesystem further includes a User Enhanced Guidance (UEG) module thatprovides personalized guided augmented reality awareness.

The RCR determination module 125 may, based on the dynamic environmentdata 105, determine situations where there is a likelihood of roadwaycomplexity associated with construction or other dynamic situationsencountered while driving the vehicle using onboard sensors (e.g., avehicle sensory system) and connectivity information received from theC-I2V data 110. For example, the C-I2V data be received byinfrastructure having computing elements that send messages to nearbyvehicles indicative of the presence of traffic barriers and road shiftson the roadway. In other aspects, the C-I2V data may include dataassociated with a vehicle collision, including information thatidentifies one or more traffic lanes that are open or not open forthrough traffic, traffic speed information, time estimates for clearingthe roadway complexity, and other similar information.

According to other embodiments, the RCR determination module 125 caninclude computational intelligence that incorporates a multi-inputcomputational approach to determine the likelihood of road complexityusing information sensed by the vehicle sensory system. For example, theRCR determination module 125 may determine the time-to-roadway laneshifts or barriers using the telematics connectivity data 115 and/or thevehicle, driver, environment data 110, and obtain information about thetime of day, weather conditions, and other factors to provide predictiveinformation. The RCR determination module 125 may receive system inputsindicative of vehicle localization information and route information,and determine the likelihood of encountering the roadway complexitygiven the present or average vehicle speed received as part of thetelematics connectivity data 115. The RCR determination module 125 mayanalyze relative positioning and route, and determine if the vehicle isapproaching an area having known roadway complexity associated with theI2V data 112. The RCR determination module 125 may determine theproximity of the vehicle with respect to the road complexity, or basedon a location of the sign indicating the road complexity, and/or basedon the vehicle's closing velocity. The RCR determination module 125 mayforward this information as output to the domain assessment block 130for processing by the TAF module 135.

The TAF module 135 is a decision-making sub-system programmed and/orconfigured to provide input to the User Enhanced Guidance (UEG) module(140) to determine appropriate guidance output to the vehicle driver(user 150). The guidance output can alert the user 150 with one or moreautomated awareness messages associated with the complex roadway.Example output can include an image of a roadway sign, speed variationinstruction such as slow down ahead, merge right ahead, merge left in Nmeters, detour ahead, etc. In another embodiment, the output message mayinclude an auditory indication that accompanies a visual indication. Thevisual and audio indication may be delivered using an augmented reality(AR) delivery system such as a heads-up display, and/or via a wearableAR device such as AR glasses or the like.

The TAF module 135 can determine and select a driver-specific andsituation-specific form of messaging based on information obtained bythe RCR determination module 125. For example, the TAF module 135 maytrack vehicle speed, vehicle location, and user 150 activity todetermine appropriate user guidance, based on information received fromthe RCR determination module 125. The guidance may be the TAF appliesone or more decision rules to determine the activation of the relevantAR rendering.

The TAF applies decision rules to determine and provide input to the UEGfor the activation of the relevant AR rendering. For example a generalrule may be of the form:

If(u _(i)>RCR_time>w _(i), and V _(spd) is >=x _(i), and y _(i)<UA<z_(i))  (1)

Then TAF_output=m _(i)  (2)

Where RCR_time is the computed tracked time to roadway complexity(obtained from the RCR). The variables u_(i) and w_(i) are tunablethresholds, for example 5 secs and 0.5 secs respectively. V_(spd) is thevehicle speed, and x_(i) is a selectable threshold, for example 10 kmph.UA is a user activity value (between 0-1, which may be determined basedon accelerometer, steering, and brake actions, respectively). Thevariables y_(i) and z_(i) are selectable thresholds, for example, 0.3and 0.7, respectively. The TAF_output m_(i) is the output sent to theUEG personalization block 140. The value m_(i) may include one of threevalues 1, 2, or 0, representing the user selected action should adecision for AR rendering be made. The value 1 represents that an ARrendering should be provided automatically, 2 indicates that arecommendation should be made, while 0 indicates that no AR renderingwould be provided.

The UEG personalization block 140 functionally represents an augmentedreality interface sub-system that utilizes a UEG module 145 to providefeedback to the user from information received from the TAF module 135.The UEG module 145 may provide a graduated feedback that can bepersonalized to the user for determining upcoming roadway complexityfrom barriers and lane shifts crossings. The UEG module 145 may providepersonalized feedback according to the following logic:

$\begin{matrix}\left( {= \begin{Bmatrix}{low} & {if} & {< \leq} \\{medium} & {if} & {< \leq} \\ & {if} & \leq \end{Bmatrix}} \right. & (3)\end{matrix}$

where T1, and T2, and T3 are example Time to the Roadway Complexity(T_RC) thresholds that can be adjusted based on driver type andtime-of-day; and UEG(pers.) is personalized user feedback having one ofthree values: low, which includes minimal AR rendering; medium whichincludes AR rendering and sound, and high which includes AR rendering,sound, and tactile feedback.

In one example embodiment, increased intensity from visual andadditional elements may not be provided if the UEG module 145 determinesthat the user 150 is decelerating and responsive to observing theroadway complexity. In addition, an additional alert may be provided bythe UEG module 145 responsive to determining that the thresholdconditions of equation (3) are satisfied.

FIG. 2 illustrates an example computing environment in which techniquesand structures for providing the systems and methods disclosed hereinmay be implemented. More particularly, FIG. 2 depicts an examplecomputing environment 200 that can include a vehicle 205. The vehicle205 may include an automotive computer 245, a roadway complexityawareness processor 208 in communication with the automotive computer245, and a Vehicle Controls Unit (VCU) 265 that can include a pluralityof electronic control units (ECUs) 217 disposed in communication withthe automotive computer 245 and/or the roadway complexity awarenessprocessor 208. A mobile device 220, which may be associated with theuser 150 and the vehicle 205, may connect with the automotive computer245 using wired and/or wireless communication protocols andtransceivers. The mobile device 220 may be communicatively coupled withthe vehicle 205 via one or more network(s) 225, which may communicatevia one or more wireless connection(s) 230, and/or may connect with thevehicle 205 directly using near field communication (NFC) protocols,Bluetooth® protocols, Wi-Fi, Ultra-Wide Band (UWB), and other possibledata connection and sharing techniques. In one embodiment, and inaddition to AR, the AR messages delivered via the processor 208, themobile device 220 may also output one or more of the messages using astandard interface of the mobile device.

The vehicle 205 may also receive and/or be in communication with aGlobal Positioning System (GPS) 275. The GPS 275 may be a satellitesystem (as depicted in FIG. 2) such as the global navigation satellitesystem (GLNSS), Galileo, or navigation or other similar system. In otheraspects, the GPS 275 may be a terrestrial-based navigation network. Insome embodiments, the vehicle 205 may utilize a combination of GPS andDead Reckoning responsive to determining that a threshold number ofsatellites are not recognized. The GPS 275 may provide means for vehiclesensory system information that can include a present localization ofthe vehicle 205, trip destination information, average vehicle speeds,traffic information, and/or other real-time information.

The automotive computer 245 may be or include an electronic vehiclecontroller, having one or more processor(s) 250 and memory 255. Theautomotive computer 245 may, in some example embodiments, be disposed incommunication with the mobile device 220, and one or more server(s) 270.The server(s) 270 may be part of a cloud-based computing infrastructure,and may be associated with and/or include a Telematics Service DeliveryNetwork (SDN) that provides digital data services to the vehicle 205 andother vehicles (not shown in FIG. 2) that may be part of a vehiclefleet.

Although illustrated as a sport utility, the vehicle 205 may take theform of another passenger or commercial automobile such as, for example,a car, a truck, a crossover vehicle, a van, a minivan, a taxi, a bus,etc., and may be configured and/or programmed to include various typesof automotive drive systems. Example drive systems can include varioustypes of internal combustion engines (ICEs) powertrains having agasoline, diesel, or natural gas-powered combustion engine withconventional drive components such as, a transmission, a drive shaft, adifferential, etc. In another configuration, the vehicle 205 may beconfigured as an electric vehicle (EV). More particularly, the vehicle205 may include a battery EV (BEV) drive system, or be configured as ahybrid EV (HEV) having an independent onboard powerplant, a plug-in HEV(PHEV) that includes an HEV powertrain connectable to an external powersource, and/or includes a parallel or series hybrid powertrain having acombustion engine powerplant and one or more EV drive systems. HEVs mayfurther include battery and/or supercapacitor banks for power storage,flywheel power storage systems, or other power generation and storageinfrastructure. The vehicle 205 may be further configured as a fuel cellvehicle (FCV) that converts liquid or solid fuel to usable power using afuel cell, (e.g., a hydrogen fuel cell vehicle (HFCV) powertrain, etc.)and/or any combination of these drive systems and components.

Further, the vehicle 205 may be a manually driven vehicle, and/or beconfigured and/or programmed to operate in a fully autonomous (e.g.,driverless) mode (e.g., Level-5 autonomy) or in one or more partialautonomy modes, which may include driver assist technologies.

The mobile device 220 can include a memory 223 for storing programinstructions associated with an application 235 that, when executed by amobile device processor 221, performs aspects of the disclosedembodiments. The application (or “app”) 235 may be part of the roadwaycomplexity awareness system 100, or may provide information to theroadway complexity awareness system 100 and/or receive information fromthe roadway complexity awareness system 100.

In some aspects, the mobile device 220 may communicate with the vehicle205 through the one or more wireless connection(s) 230, which may beencrypted or non-encrypted, and established between the mobile device220 and a Telematics Control Unit (TCU) 260. The mobile device 220 maycommunicate with the TCU 260 using a wireless transmitter (not shown inFIG. 2) associated with the TCU 260 on the vehicle 205. The transmittermay communicate with the mobile device 220 using a wirelesscommunication network such as, for example, the one or more network(s)225. The wireless connection(s) 230 are depicted in FIG. 2 ascommunicating via the one or more network(s) 225, and via one or morewireless connection(s) 233 that can be direct connection(s) between thevehicle 205 and the mobile device 220. The wireless connection(s) 233may include various low-energy protocols, including, for example,Bluetooth®, Bluetooth® Low-Energy (BLE®), UWB, Near Field Communication(NFC), or other protocols.

The network(s) 225 illustrate an example communication infrastructure inwhich the connected devices discussed in various embodiments of thisdisclosure may communicate. The network(s) 225 may be and/or include theInternet, a private network, public network or other configuration thatoperates using any one or more known communication protocols such as,for example, transmission control protocol/Internet protocol (TCP/IP),Bluetooth®, BLE®, Wi-Fi based on the Institute of Electrical andElectronics Engineers (IEEE) standard 802.11, UWB, and cellulartechnologies such as Time Division Multiple Access (TDMA), Code DivisionMultiple Access (CDMA), High-Speed Packet Access (HSPDA), Long-TermEvolution (LTE), Global System for Mobile Communications (GSM), andFifth Generation (5G), to name a few examples.

The automotive computer 245 and/or the roadway complexity awarenessprocessor 208 may be installed in an engine compartment of the vehicle205 (or elsewhere in the vehicle 205) and operate as a functional partof the roadway complexity awareness system 100, in accordance with thedisclosure. The automotive computer 245 may include one or moreprocessor(s) 250 and a computer-readable memory 255. Moreover, theroadway complexity awareness processor 208 may be separate from theautomotive computer 245, or may be integrated as part of the automotivecomputer 245.

The one or more processor(s) 250 may be disposed in communication withone or more memory devices disposed in communication with the respectivecomputing systems (e.g., the memory 255 and/or one or more externaldatabases not shown in FIG. 2). The processor(s) 250 may utilize thememory 255 to store programs in code and/or to store data for performingaspects in accordance with the disclosure. The memory 255 may be anon-transitory computer-readable memory storing a roadway complexityawareness program code. The memory 255 can include any one or acombination of volatile memory elements (e.g., dynamic random accessmemory (DRAM), synchronous dynamic random-access memory (SDRAM), etc.)and can include any one or more nonvolatile memory elements (e.g.,erasable programmable read-only memory (EPROM), flash memory,electronically erasable programmable read-only memory (EEPROM),programmable read-only memory (PROM), etc.

The VCU 265 may share a power bus 278 with the automotive computer 245,and may be configured and/or programmed to coordinate the data betweenvehicle 205 systems, connected servers (e.g., the server(s) 270), andother vehicles (not shown in FIG. 2) operating as part of a vehiclefleet. The VCU 265 can include or communicate with any combination ofthe ECUs 217, such as, for example, a Body Control Module (BCM) 293, anEngine Control Module (ECM) 285, a Transmission Control Module (TCM)290, the TCU 260, a Driver Assistances Technologies (DAT) controller299, etc. The VCU 265 may further include and/or communicate with aVehicle Perception System (VPS) 281, having connectivity with and/orcontrol of one or more vehicle sensory system(s) 282. In some aspects,the VCU 265 may control operational aspects of the vehicle 205, andimplement one or more instruction sets received from the application 235operating on the mobile device 220, from one or more instruction setsstored in computer memory 255 of the automotive computer 245, includinginstructions operational as part of the roadway complexity awarenesssystem 100.

The TCU 260 can be configured and/or programmed to provide vehicleconnectivity to wireless computing systems onboard and offboard thevehicle 205, and may include a Navigation (NAV) receiver 288 forreceiving and processing a GPS signal from the GPS 275, a BLE® Module(BLEM) 295, a Wi-Fi transceiver, a UWB transceiver, and/or otherwireless transceivers (not shown in FIG. 2) that may be configurable forwireless communication between the vehicle 205 and other systems,computers, and modules. The TCU 260 may be disposed in communicationwith the ECUs 217 by way of a bus 280 (not shown). In some aspects, theTCU 260 may retrieve data and send data as a node in a CAN bus.

According to other embodiments, the TCU 260 may provide connectivity toinfrastructure elements in the driving environment, such as roadsidecomputing modules (not shown), that are stationary devices operating aspart of the roadway infrastructure. In other aspects, the infrastructuredevices may be associated with roadway signs, barriers, or otherdevices, and provide information to the TCU such as roadway complexityinformation, roadway status information, route change information,and/or the like.

The BLEM 295 may establish wireless communication using Bluetooth® andBLE® communication protocols by broadcasting and/or listening forbroadcasts of small advertising packets, and establishing connectionswith responsive devices that are configured according to embodimentsdescribed herein. For example, the BLEM 295 may include GenericAttribute Profile (GATT) device connectivity for client devices thatrespond to or initiate GATT commands and requests, and connect directlywith the mobile device 220.

The bus 280 may be configured as a Controller Area Network (CAN) busorganized with a multi-master serial bus standard for connecting two ormore of the ECUs 217 as nodes using a message-based protocol that can beconfigured and/or programmed to allow the ECUs 217 to communicate witheach other. The bus 280 may be or include a high speed CAN (which mayhave bit speeds up to 1 Mb/s on CAN, 5 Mb/s on CAN Flexible Data Rate(CAN FD)), and can include a low-speed or fault-tolerant CAN (up to 125Kbps), which may, in some configurations, use a linear busconfiguration. In some aspects, the ECUs 217 may communicate with a hostcomputer (e.g., the automotive computer 245, the roadway complexityawareness system 100, and/or the server(s) 270, etc.), and may alsocommunicate with one another without the necessity of a host computer.The bus 280 may connect the ECUs 217 with the automotive computer 245such that the automotive computer 245 may retrieve information from,send information to, and otherwise interact with the ECUs 217 to performsteps described according to embodiments of the present disclosure. Thebus 280 may connect CAN bus nodes (e.g., the ECUs 217) to each otherthrough a two-wire bus, which may be a twisted pair having a nominalcharacteristic impedance. The bus 280 may also be accomplished usingother communication protocol solutions, such as Media Oriented SystemsTransport (MOST) or Ethernet. In other aspects, the bus 280 may be awireless intra-vehicle bus.

The VCU 265 may control various loads directly via the bus 280communication or implement such control in conjunction with the BCM 293.The ECUs 217 described with respect to the VCU 265 are provided forexample purposes only, and are not intended to be limiting or exclusive.Control and/or communication with other control modules not shown inFIG. 2 is possible, and such control is contemplated.

In an example embodiment, the ECUs 217 may control aspects of vehicleoperation and communication using inputs from human drivers, inputs froman autonomous vehicle controller, the roadway complexity awarenesssystem 100, and/or via wireless signal inputs received via the wirelessconnection(s) 233 from other connected devices such as the mobile device220, among others. The ECUs 217, when configured as nodes in the bus280, may each include a central processing unit (CPU), a CAN controller,and/or a transceiver (not shown in FIG. 2). For example, although themobile device 220 is depicted in FIG. 2 as connecting to the vehicle 205via the BLEM 295, it is possible and contemplated that the wirelessconnection 233 may also or alternatively be established between themobile device 220 and one or more of the ECUs 217 via the respectivetransceiver(s) associated with the module(s).

The BCM 293 generally includes integration of sensors, vehicleperformance indicators, and variable reactors associated with vehiclesystems, and may include processor-based power distribution circuitrythat can control functions associated with the vehicle body such aslights, windows, security, door locks and access control, and variouscomfort controls. The BCM 293 may also operate as a gateway for bus andnetwork interfaces to interact with remote ECUs (not shown in FIG. 2).

The BCM 293 may coordinate any one or more functions from a wide rangeof vehicle functionality, including energy management systems, alarms,vehicle immobilizers, driver and rider access authorization systems,Phone-as-a-Key (PaaK) systems, driver assistance systems, AV controlsystems, power windows, doors, actuators, and other functionality, etc.The BCM 293 may be configured for vehicle energy management, exteriorlighting control, wiper functionality, power window, and doorfunctionality, heating ventilation and air conditioning systems, anddriver integration systems. In other aspects, the BCM 293 may controlauxiliary equipment functionality, and/or be responsible for integrationof such functionality.

The DAT controller 299 may provide Level-1 through Level-3 automateddriving and driver assistance functionality that can include, forexample, active parking assistance, trailer backup assistance, adaptivecruise control, lane keeping, and/or driver status monitoring, amongother features. The DAT controller 299 may also provide aspects of userand environmental inputs usable for user authentication. Authenticationfeatures may include, for example, biometric authentication andrecognition.

The DAT controller 299 can obtain input information via the sensorysystem(s) 282, which may include sensors disposed on the vehicleinterior and/or exterior (sensors not shown in FIG. 2). The DATcontroller 299 may receive the sensor information associated with driverfunctions, vehicle functions, and environmental inputs, and otherinformation. The DAT controller 299 may characterize the sensorinformation for identification of biometric markers stored in a securebiometric data vault (not shown in FIG. 2) onboard the vehicle 205and/or via the server(s) 270.

In other aspects, the DAT controller 299 may also be configured and/orprogrammed to control Level-1 and/or Level-2 driver assistance when thevehicle 205 includes Level-1 or Level-2 autonomous vehicle drivingfeatures. The DAT controller 299 may connect with and/or include aVehicle Perception System (VPS) 281, which may include internal andexternal sensory systems (collectively referred to as sensory systems281). The sensory systems 282 may be configured and/or programmed toobtain sensor data usable for biometric authentication, and forperforming driver assistances operations such as, for example, activeparking, trailer backup assistances, adaptive cruise control, and lanekeeping, driver status monitoring, and/or other features.

For example, the DAT controller 299 may determine driver informationindicative of one or more driver characteristics such as driver age, adriving experience estimation value for the vehicle operator, and/or adriving attention value indicative of a present amount of attention thevehicle operator gives to operating the vehicle. The DAT controller 299may determine driver age by identifying a particular driver viabiometric authentication, via a fob (not shown) associated with thedriver (user 150), or via other means known in the art for personallyidentifying driver identification characteristics.

In another aspect, the DAT controller may estimate driving experiencebased on driver age and other factors such as a driver license number,driver profile information provided to the application using a vehicleor external computer, or via other means such as observation of drivingcharacteristics such as braking actions per minute, steering actions perminute, and use history of that driver with the vehicle 205.

According to another embodiment, the roadway complexity awarenessprocessor 208, although shown in FIG. 2 as connecting to the bus 280,may be integrated with and/or executed as part of the DAT 299. Forexample, the DAT 299 may receive roadway status information from aninfrastructure processor associated with the roadway, obtain sensoryinformation from the VPS 281 indicative of roadway route complexity, anddetermine a likelihood of roadway route complexity based on the roadwaystatus information and the sensory information. In other aspects, theDAT 299 may select an AR message indicative of the roadway routecomplexity and generate the AR message using an AR interface, where theAR message is visible to the user 150 using an augmented realityinterface device 246. The DAT 299 may also determine other informationsuch as current weather conditions and time of day, which may be usedfor determining roadway complexity likelihood values that serve asinputs for selection of the AR message intensity level to be deliveredto the vehicle user 150 via a heads-up display 247, or using a wearableor non-wearable AR rendering device (not shown in FIG. 2).

The computing system architecture of the automotive computer 245, theVCU 265, and/or the roadway complexity awareness system 100 may omitcertain computing modules. It should be readily understood that thecomputing environment depicted in FIG. 2 is an example of a possibleimplementation according to the present disclosure, and thus, it shouldnot be considered limiting or exclusive.

The roadway complexity awareness processor 208, regardless of whether itis integrated with the automotive computer 245, the DAT 299, another ofthe VCU 265, or operate as an independent computing system in thevehicle 205, may include a processor 249, and a computer-readable memory248. The processor 249 may instantiate one or more modules such as theUEG 145, the TAF module 135, and/or the RCR determination module 125based on instruction sets stored in the memory 248.

The automotive computer 245 may connect with an infotainment system 210that may provide an interface for the navigation and GPS receiver, andthe roadway complexity awareness system 100. The infotainment system 210may include a touchscreen interface portion 211, and may include voicerecognition features, biometric identification capabilities that canidentify users based on facial recognition, voice recognition,fingerprint identification, or other biological identification means. Inother aspects, the infotainment system 210 may provide useridentification using mobile device pairing techniques (e.g., connectingwith the mobile device 220, a Personal Identification Number (PIN))code, a password, passphrase, or other identifying means.

FIG. 3 depicts an example view of a roadway as observed by a user (notshown in FIG. 3 for clarity) of the vehicle 205 in accordance with thepresent disclosure. A heads-up display 247 disposed proximate to orintegrated a vehicle windshield may allow a direct view of approachingroadway complexities 305, which are provided in the example of FIG. 3 asa road block that will force a stop or a right turn as the vehicleapproaches the position of the roadway complexity 305.

FIG. 4 depicts an example message delivered to a vehicle operator (notshown in FIG. 4) using the roadway complexity awareness system 100, inaccordance with the present disclosure. For example, as the vehicle 205approaches the roadway complexity 305, the roadway complexity awarenesssystem 100 may output an AR message 405 using the AR interface device246, where the AR message is visible to the user 150. For example, theAR message may include a written description of the roadway complexity(in the present example, “Road Closed”), a symbol or graphicdemonstrating the type of complexity (e.g., the road barrierrepresentation 415), and/or directional graphics or animations 420 thatindicate recommended vehicle actions (e.g., turn right or left, veerright or left, stop vehicle, slow vehicle, etc.

FIG. 5 depicts a flow diagram of an example method for generating anaugmented reality message using the system of FIG. 1 in accordance withthe present disclosure.

FIG. 5 is a flow diagram of an example method 500 for controlling aroadway complexity awareness system in a vehicle, according to thepresent disclosure. FIG. 5 may be described with continued reference toprior figures, including FIGS. 1-4. The following process is exemplaryand not confined to the steps described hereafter. Moreover, alternativeembodiments may include more or less steps that are shown or describedherein, and may include these steps in a different order than the orderdescribed in the following example embodiments.

Referring first to FIG. 5, at step 505, the method 500 may commence withreceiving roadway status information from an infrastructure processorassociated with a roadway.

At step 510, the method 500 may further include obtaining, via theprocessor from a vehicle sensory system, sensory information indicativeof roadway route complexity. This step may include receiving, from anI2V device, information indicative of the type of roadway complexity,location of the roadway complexity, and other information. According toanother embodiment, the system may obtain information indicative of theroadway complexity using onboard sensors that may not be connected toI2V computing systems. For example, the VPS may determine the presenceof a sign, barrier, vehicle collision, or other sensory information thatindicates presence and location of roadway complexity.

According to another embodiment, the roadway complexity may be receivedvia a vehicle-to-vehicle information network (not shown), where aproximate vehicle determines the presence of the road complexity andpasses that information to the vehicle 205.

At step 515, the method 500 may further include determining, via theprocessor, a likelihood of roadway route complexity based on the roadwaystatus information and the sensory information. This step may includereceiving vehicle operation data comprising one or more of a vehiclespeed, a steering action, and a brake action. For example, the processor249 may cause the RCR determination module 124 to obtain, from the VPS281, the vehicle speed, steering action information, and brake actioninformation. In another aspect, this step may include receiving, via theprocessor, route information comprising a trip destination and a presentlocalization of the vehicle.

In one example, the RCR determination module may obtain, via the TCU,present vehicle localization, determine time-to-roadway calculations,and determine other conditions such as weather conditions to providepredictive information to the TAF module 135.

In another aspect this step may include obtaining, via the processor,driver information indicative of one or more driver characteristics.Accordingly, this stem may further include generating, using the vehicleoperation data, the route information, and the driver information, aroadway complexity value indicative of the likelihood of roadway routecomplexity.

This step may further include determining the age of the vehicleoperator, determining a driving experience estimation value for thevehicle operator, and determining a driving attention value indicativeof a present amount of attention the vehicle operator gives to operatingthe vehicle. The information may be used for determining, based on arelative localization and the route information that the vehicle isapproaching an area associated with the roadway complexity, anddetermining the roadway complexity likelihood value based on therelative localization and the route information.

At step 520, the method 500 may further include selecting, via theprocessor, an augmented reality message indicative of the roadway routecomplexity. This step may include selecting the message intensity levelbased on one or more of selecting the augmented reality messagecomprises:

Selecting, via the processor, a message intensity level based on one ofa low time to the roadway complexity, a medium time to the roadwaycomplexity, and a high time to roadway complexity.

At step 525, the method 500 may further include generating, via theprocessor, the augmented reality message using an augmented realityinterface device, wherein the augmented reality message is visible to avehicle operator. This step may include displaying the AR message on anAR heads-up display of the vehicle, and/or displaying the AR messageusing one or more connected wearable or non-wearable AR devices.

In the above disclosure, reference has been made to the accompanyingdrawings, which form a part hereof, which illustrate specificimplementations in which the present disclosure may be practiced. It isunderstood that other implementations may be utilized, and structuralchanges may be made without departing from the scope of the presentdisclosure. References in the specification to “one embodiment,” “anembodiment,” “an example embodiment,” etc., indicate that the embodimentdescribed may include a particular feature, structure, orcharacteristic, but every embodiment may not necessarily include theparticular feature, structure, or characteristic. Moreover, such phrasesare not necessarily referring to the same embodiment. Further, when afeature, structure, or characteristic is described in connection with anembodiment, one skilled in the art will recognize such feature,structure, or characteristic in connection with other embodimentswhether or not explicitly described.

Further, where appropriate, the functions described herein can beperformed in one or more of hardware, software, firmware, digitalcomponents, or analog components. For example, one or more applicationspecific integrated circuits (ASICs) can be programmed to carry out oneor more of the systems and procedures described herein. Certain termsare used throughout the description and claims refer to particularsystem components. As one skilled in the art will appreciate, componentsmay be referred to by different names. This document does not intend todistinguish between components that differ in name, but not function.

It should also be understood that the word “example” as used herein isintended to be non-exclusionary and non-limiting in nature. Moreparticularly, the word “example” as used herein indicates one amongseveral examples, and it should be understood that no undue emphasis orpreference is being directed to the particular example being described.

A computer-readable medium (also referred to as a processor-readablemedium) includes any non-transitory (e.g., tangible) medium thatparticipates in providing data (e.g., instructions) that may be read bya computer (e.g., by a processor of a computer). Such a medium may takemany forms, including, but not limited to, non-volatile media andvolatile media. Computing devices may include computer-executableinstructions, where the instructions may be executable by one or morecomputing devices such as those listed above and stored on acomputer-readable medium.

With regard to the processes, systems, methods, heuristics, etc.described herein, it should be understood that, although the steps ofsuch processes, etc. have been described as occurring according to acertain ordered sequence, such processes could be practiced with thedescribed steps performed in an order other than the order describedherein. It further should be understood that certain steps could beperformed simultaneously, that other steps could be added, or thatcertain steps described herein could be omitted. In other words, thedescriptions of processes herein are provided for the purpose ofillustrating various embodiments and should in no way be construed so asto limit the claims.

Accordingly, it is to be understood that the above description isintended to be illustrative and not restrictive. Many embodiments andapplications other than the examples provided would be apparent uponreading the above description. The scope should be determined, not withreference to the above description, but should instead be determinedwith reference to the appended claims, along with the full scope ofequivalents to which such claims are entitled. It is anticipated andintended that future developments will occur in the technologiesdiscussed herein, and that the disclosed systems and methods will beincorporated into such future embodiments. In sum, it should beunderstood that the application is capable of modification andvariation.

All terms used in the claims are intended to be given their ordinarymeanings as understood by those knowledgeable in the technologiesdescribed herein unless an explicit indication to the contrary is madeherein. In particular, use of the singular articles such as “a,” “the,”“said,” etc., should be read to recite one or more of the indicatedelements unless a claim recites an explicit limitation to the contrary.Conditional language, such as, among others, “can,” “could,” “might,” or“may,” unless specifically stated otherwise, or otherwise understoodwithin the context as used, is generally intended to convey that certainembodiments could include, while other embodiments may not include,certain features, elements, and/or steps. Thus, such conditionallanguage is not generally intended to imply that features, elements,and/or steps are in any way required for one or more embodiments.

That which is claimed is:
 1. A method for controlling a vehicle toprovide roadway complexity awareness messages, comprising: receivingroadway status information from an infrastructure processor associatedwith a roadway; obtaining, via the processor from a vehicle sensorysystem, sensory information indicative of roadway route complexity;determining, via the processor, a likelihood of roadway route complexitybased on the roadway status information and the sensory information;selecting, via the processor, an augmented reality message indicative ofthe roadway route complexity; and generating, via the processor, theaugmented reality message using an augmented reality interface device,wherein the augmented reality message is visible to a vehicle operator.2. The method according to claim 1, wherein determining the likelihoodof roadway route complexity comprises: receiving vehicle operation datacomprising one or more of a vehicle speed, a steering action, and abrake action; receiving, via the processor, roadway complexityinformation from the infrastructure processor providinginfrastructure-to-vehicle communication associated with the roadway;receiving, via the processor, route information comprising a tripdestination and a present localization of the vehicle; obtaining, viathe processor, driver information indicative of one or more drivercharacteristics; and generating, using the vehicle operation data,information from the infrastructure-to-vehicle communication, the routeinformation, and the driver information, a roadway complexity valueindicative of the likelihood of roadway route complexity.
 3. The methodaccording to claim 2, wherein obtaining the driver informationcomprises: determining an age of the vehicle operator; determining adriving experience estimation value for the vehicle operator; anddetermining a driving attention value indicative of a present amount ofattention the vehicle operator gives to operating the vehicle.
 4. Themethod according to claim 2, further comprising: determining, based on arelative localization and the route information that the vehicle isapproaching an area associated with the roadway complexity; anddetermining the roadway complexity likelihood value based on therelative localization and the route information.
 5. The method accordingto claim 4, further comprising: determining a time of day; anddetermining the roadway complexity likelihood value further based on thetime of day.
 6. The method according to claim 4, further comprising.determining a weather condition; and determining the roadway complexitylikelihood value further based on the weather condition.
 7. The methodaccording to claim 1, wherein selecting the augmented reality message,sound, and tactile feedback comprises: selecting, via the processor, amessage intensity level based on one of: a low time to the roadwaycomplexity; a medium time to the roadway complexity; and a high time toroadway complexity.
 8. A roadway complexity awareness system for avehicle, comprising: a processor; an augmented reality interfacedisposed in communication with the processor; and a memory for storingexecutable instructions, the processor programmed to execute theinstructions to: receive roadway status information from aninfrastructure processor associated with a roadway; obtain, from avehicle sensory system, sensory information indicative of roadway routecomplexity; determine a likelihood of roadway route complexity based onthe roadway status information and the sensory information; select anaugmented reality message indicative of the roadway route complexity;and generate the augmented reality message using an augmented realityinterface device, wherein the augmented reality message is visible to avehicle operator.
 9. The system according to claim 8, wherein theprocessor is further programmed to determine the likelihood of roadwayroute complexity by executing the instructions to: receive vehicleoperation data comprising one or more of a vehicle speed, a steeringaction, and a brake action; receive information from the infrastructureprocessor providing infrastructure-to-vehicle communication associatedwith the roadway; receive route information comprising a tripdestination and a present localization of the vehicle; obtain driverinformation indicative of one or more driver characteristics; andgenerate, using the vehicle operation data, information from theinfrastructure-to-vehicle communication, the route information, and thedriver information, a roadway complexity value indicative of thelikelihood of roadway route complexity.
 10. The system according toclaim 9, wherein the processor is further programmed to obtain thedriver information by executing the instructions to: determine an age ofthe vehicle operator; determine a driving experience estimation valuefor the vehicle operator; and determine a driving attention valueindicative of a present amount of attention the vehicle operator givesto operating the vehicle.
 11. The system according to claim 9, whereinthe processor is further programmed to execute the instructions to:determine, based on a relative localization and the route informationthat the vehicle is approaching an area associated with the roadwaycomplexity; and determine the roadway complexity likelihood value basedon the relative localization and the route information.
 12. The systemaccording to claim 11, wherein the processor is further programmed toexecute the instructions to: determine a time of day; and determine theroadway complexity likelihood value further based on the time of day.13. The system according to claim 11, wherein the processor is furtherprogrammed to execute the instructions to: determine a weathercondition; and determine the roadway complexity likelihood value furtherbased on the weather condition.
 14. The system according to claim 8,wherein the processor is further programmed to select the augmentedreality message, sound, and tactile feedback by executing theinstructions to: select a message intensity level based on one of: a lowtime to the roadway complexity; a medium time to the roadway complexity;and a high time to roadway complexity.
 15. A non-transitorycomputer-readable storage medium in a roadway complexity awarenessprocessor, the computer-readable storage medium having instructionsstored thereupon which, when executed by a processor, cause theprocessor to: receive roadway status information from an infrastructureprocessor associated with a roadway; obtain, from a vehicle sensorysystem, sensory information indicative of roadway route complexity;determine a likelihood of roadway route complexity based on the roadwaystatus information and the sensory information; select an augmentedreality message indicative of the roadway route complexity; and generatethe augmented reality message using an augmented reality interface,wherein the augmented reality message is visible to a vehicle operator.16. The non-transitory computer-readable storage medium according toclaim 15, having further instructions stored thereupon to: receivevehicle operation data comprising one or more of a vehicle speed, asteering action, and a brake action; and receive roadway complexityinformation from the infrastructure processor providinginfrastructure-to-vehicle communication associated with the roadway;receive route information comprising a trip destination and a presentlocalization of the vehicle; and obtain driver information indicative ofone or more driver characteristics; generate, using the vehicleoperation data, roadway complexity information from theinfrastructure-to-vehicle communication, the route information, and thedriver information, a roadway complexity value indicative of thelikelihood of roadway route complexity.
 17. The non-transitorycomputer-readable storage medium according to claim 16, having furtherinstructions stored thereupon to: determine an age of the vehicleoperator; determine a driving experience estimation value for thevehicle operator; and determine a driving attention value indicative ofa present amount of attention the vehicle operator gives to operatingthe vehicle.
 18. The non-transitory computer-readable storage mediumaccording to claim 16, having further instructions stored thereupon to:determine, based on a relative localization and the route informationthat the vehicle is approaching an area associated with the roadwaycomplexity; and determine the roadway complexity likelihood value basedon the relative localization and the route information.
 19. Thenon-transitory computer-readable storage medium according to claim 18,having further instructions stored thereupon to: determine a time ofday; and determine the roadway complexity likelihood value further basedon the time of day.
 20. The non-transitory computer-readable storagemedium according to claim 15, having further instructions storedthereupon to: select a message intensity level based on one of: a lowtime to the roadway complexity; a medium time to the roadway complexity;and a high time to roadway complexity.